Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Communications of the ACM
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Data mining, hypergraph transversals, and machine learning (extended abstract)
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Mining needle in a haystack: classifying rare classes via two-phase rule induction
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Making use of the most expressive jumping emerging patterns for classification
Knowledge and Information Systems
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
Machine Learning
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
An Efficient Single-Scan Algorithm for Mining Essential Jumping Emerging Patterns for Classification
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Fast Algorithms for Mining Emerging Patterns
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
A Bayesian approach to use emerging patterns for classification
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
DeEPs: A New Instance-Based Lazy Discovery and Classification System
Machine Learning
Incremental Maintenance on the Border of the Space of Emerging Patterns
Data Mining and Knowledge Discovery
Using Emerging Patterns and Decision Trees in Rare-Class Classification
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
Exploiting maximal emerging patterns for classification
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
A Parameter-Free Associative Classification Method
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Application-Independent Feature Construction from Noisy Samples
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Contrast pattern mining and its applications
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
On the stimulation of patterns: definitions, calculation method and first usages
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Cascading an emerging pattern based classifier
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
A framework to mine high-level emerging patterns by attribute-oriented induction
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Efficiently finding the best parameter for the emerging pattern-based classifier PCL
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Hiding emerging patterns with local recoding generalization
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Editorial: Parameter-free classification in multi-class imbalanced data sets
Data & Knowledge Engineering
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Data mining is one of the most important areas in the 21 century for its applications are wide ranging. This includes medicine, finance, commerce and engineering, to name a few. Pattern mining is amongst the most important and challenging techniques employed in data mining. Patterns are collections of items which satisfy certain properties. Emerging Patterns are those whose frequencies change significantly from one dataset to another. They represent strong contrast knowledge and have been shown very successful for constructing accurate and robust classifiers. In this paper, we examine various kinds of patterns. We also investigate efficient pattern mining techniques and discuss how to exploit patterns to construct effective classifiers.